AI-Based Radiographic Detection of Periodontal Infrabony and Furcation Defects: A Diagnostic Model Study
试验速览
- 阶段
- 不适用
- 状态
- 已完成
- 入组人数
- 500
- 试验地点
- 1
- 主要终点
- Intersection over Union (IoU)
研究概览
简要总结
The primary objective of the study is to develop and validate a machine learning model for the automatic identification of periodontal vertical bone defects, improving diagnostic accuracy and efficiency.
The study comprises three phases:
- Public dataset annotation: Approximately 7,000 intraoral radiographs will be manually annotated by experts to classify periodontal bone defects (1-wall, 2+ walls, craters, furcation involvement).
- Model training: A deep learning algorithm will be trained on the annotated images to learn automatic recognition of the defects.
- Clinical validation: The model will be tested on a dataset of 150 anonymized radiographs from 20-30 patients treated at AOU (Azienda Ospedaliero Universitaria) Cagliari, comparing its performance to expert dental evaluations.
详细描述
To address the challenge of detecting periodontal osseous defects, the study will employ the YOLOv8 (You Only Look Once versione 8) framework, a state-of-the-art deep learning model optimized for object detection tasks. This architecture is known for its balance between accuracy and inference speed, making it suitable for clinical applications that require efficient processing.
The YOLOv8l (large) variant will be selected to maximize detection accuracy, given the complexity of the task. The architecture will include:
- Backbone: a convolutional neural network (CNN) for multi-scale feature extraction;
- Neck: a feature pyramid network (FPN) to integrate spatial and semantic information across layers;
- Head: a detection module responsible for class probabilities and bounding box predictions.
Model Training
The training will be performed on a dataset consisting of approximately 406 images for training, 58 for validation, and 117 for testing. Annotations will include bounding boxes for four types of defects: 1-wall, 2+ walls, craters, and furcation involvement. The dataset will be formatted according to YOLO standards.
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Retrospective
入排标准
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •Intraoral radiographs showing presence of periodontal infrabony defects
排除标准
- •Intraoral radiographs showing without detectable presence of periodontal infrabony defects
结局指标
主要结局
Intersection over Union (IoU)
时间窗: Baseline
The IoU measures the overlap between a predicted bounding box and a ground truth bounding box. It is defined as: Area of Overlap/Area of Union; where the area of overlap is the intersection of the predicted and ground truth boxes, and the area of union is the total area covered by both boxes.
Precision (P)
时间窗: Baseline
The fraction of true positives (TP) among all predictions: T P/T P + F P High precision indicates that the model makes few false positive (FP) predictions.
Recall (R)
时间窗: Baseline
The fraction of true positives among all ground truth objects: T P/T P + F N (false negatives) High recall indicates that the model detects most ground truth objects.
次要结局
未报告次要终点
研究者
Nicola Alberto Valente, DDS, MS, PhD
Associate Professor
University of Cagliari